treNch: Ultra-Low Power Wireless Communication Protocol for IoT and Energy Harvesting

Although the number of Internet of Things devices increases every year, efforts to decrease hardware energy demands and to improve efficiencies of the energy-harvesting stages have reached an ultra-low power level. However, no current standard of wireless communication protocol (WCP) can fully address those scenarios. Our focus in this paper is to introduce treNch, a novel WCP implementing the cross-layer principle to use the power input for adapting its operation in a dynamic manner that goes from pure best-effort to nearly real time. Together with the energy-management algorithm, it operates with asynchronous transmissions, synchronous and optional receptions, short frame sizes and a light architecture that gives control to the nodes. These features make treNch an optimal option for wireless sensor networks with ultra-low power demands and severe energy fluctuations. We demonstrate through a comparison with different modes of Bluetooth Low Energy (BLE) a decrease of the power consumption in 1 to 2 orders of magnitude for different scenarios at equal quality of service. Moreover, we propose some security optimizations, such as shorter over-the-air counters, to reduce the packet overhead without decreasing the security level. Finally, we discuss other features aside of the energy needs, such as latency, reliability or topology, brought again against BLE.ECSEL Joint Undertaking through CONNECT project 737434Federal Ministry of Education & Research (BMBF)European Union's Horizon 2020 research and innovation programSpanish Ministry of Education, Culture and Sport (MECD)/FEDER-EU FPU18/01376BBVA FoundationUniversity of Granad

Trading Sensitivity for Power in an IEEE 802.15.4 Conformant Adequate Demodulator

In this work, a design of an IEEE 802.15.4 con-formant O-QPSK demodulator is proposed, which is capable of trading off receiver sensitivity for power savings. Such design can be used to meet rigid energy and power constraints for many applications in the Internet-of-Things (IoT) context. In a Body Area Network (BAN), for example, the circuits need to operate with extremely limited energy sources, while still meeting the network performance requirements. This challenge can be addressed by the paradigm of adequate computing, which trades off excessive quality of service for power or energy using approximation techniques. Three different, adjustable approximation techniques are integrated into the demodulation to trade off effective signal quantization bit-width, filtering performance, and sampling frequency for power. Such approximations impact incoming signal sensitivity of the demodulator. For detailed trade-off analysis, the proposed design is implemented in a commercial 40-nm CMOS technology to estimate power and in Python to estimate sensitivity. Simulation results show up to 64% power savings by sacrificing \tilde 7 dB sensitivity.</p

Trading Sensitivity for Power in an IEEE 802.15.4 Conformant Adequate Demodulator

In this work, a design of an IEEE 802.15.4 con-formant O-QPSK demodulator is proposed, which is capable of trading off receiver sensitivity for power savings. Such design can be used to meet rigid energy and power constraints for many applications in the Internet-of-Things (IoT) context. In a Body Area Network (BAN), for example, the circuits need to operate with extremely limited energy sources, while still meeting the network performance requirements. This challenge can be addressed by the paradigm of adequate computing, which trades off excessive quality of service for power or energy using approximation techniques. Three different, adjustable approximation techniques are integrated into the demodulation to trade off effective signal quantization bit-width, filtering performance, and sampling frequency for power. Such approximations impact incoming signal sensitivity of the demodulator. For detailed trade-off analysis, the proposed design is implemented in a commercial 40-nm CMOS technology to estimate power and in Python to estimate sensitivity. Simulation results show up to 64% power savings by sacrificing \tilde 7 dB sensitivity